Identification and validation of seven prognostic long non‑coding RNAs in oral squamous cell carcinoma

Miao,Tingting; Si,Qingzong; Wei,Yuan; Fan,Ruihong; Wang,Junjie; An,Xiaoli

doi:10.3892/ol.2020.11603

Identification and validation of seven prognostic long non‑coding RNAs in oral squamous cell carcinoma

Authors:
- Tingting Miao
- Qingzong Si
- Yuan Wei
- Ruihong Fan
- Junjie Wang
- Xiaoli An
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Affiliations: School of Stomatology, Lanzhou University, Gansu, Lanzhou 730000, P.R. China
Published online on: May 13, 2020 https://doi.org/10.3892/ol.2020.11603
Pages: 939-946

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Abstract

Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, due to poor diagnosis and treatment. There is increasing evidence that demonstrates the involvement of long non‑coding RNAs (lncRNAs) in carcinogenesis and cancer progression. Therefore, the aim of the present study was to explore potential lncRNA‑associated features of patients with OSCC as a valuable and independent prognostic biomarker. A total of 268 lncRNA expression profiles and clinical patient information on OSCC were downloaded from The Cancer Genome Atlas database. The clinical information was exploited for prescreening, using Cox regression analysis, and differentially expressed lncRNAs (DElncRNAs) were identified using edgeR software. Using the ‘caret’ package, the datasets were categorized into test datasets and training datasets, respectively. Through bioinformatics, seven prognostic DElncRNAs were selected. Using the regression coefficients, a risk score based on the seven‑DElncRNA signature was developed to assess the prognostic function of key DElncRNAs. According to the median risk score, patients were classified into high‑risk and low‑risk groups in the training and test datasets. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cut‑off point was obtained from ROC analysis. Based on the optimal cut‑off point, the patients were also categorized into high‑risk and low‑risk groups. Notably, the optimal cut‑off point was more sensitive than the median risk score, particularly in the test dataset. The Kaplan‑Meier survival and log rank test analysis results indicated that the P‑value, based on the optimal cut‑off, was less than the median risk cut‑off. Additionally, stratified analysis results revealed that the seven‑DElncRNAs signature was also independent of OSCC age. Furthermore, the findings of the present study suggested that the seven‑DElncRNA signature can be used as a potential prognostic indicator and may have important clinical significance in OSCC.

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